The quantitative assessment of the performance of our approaches comprises an evaluation with respect to their accuracy in sec:Experiments-QuantitativeAccuracy, as well as studies on the effects of the subpixel disparity refinement in sec:Experiments-QuantitativeRefinement and the improvements gained by an accurate left-right consistency check in sec:Experiments-QuantitativeConsistency. For the evaluation of the accuracy of our approach and its different configurations, we have used the training set of the KITTI 2015 stereo benchmark [1]}, which consists of 200 stereo image pairs and ground truth disparity maps captured by a *LIDAR sensor from on top of a car driving around urban areas, as well as the Middlebury 2014 stereo benchmark [2]}. The latter one allows a more thorough evaluation on the accuracy of our approach, and the effects of different optimizations in the processing pipeline, since it consists of 15 high-resolution stereo pairs of indoor scenes, together with highly accurate and dense ground truth disparity maps captured by a structured light sensor. Moreover, in sec:Experiments-QuantitativeSpeed, we study the processing speed and power consumption of our approaches, together with the effects of reducing the aggregation paths of the *SGM optimization. For our experiments, we have deployed our approaches on the NVIDIA Jetson Xavier AGX with an 8-core 64 bit ARMv8.2 CPU and a 512-core Volta GPU. All measurements with respect to accuracy, timings and power consumption were done on this hardware.
